This document discusses different levels of protein structure from primary to quaternary structure. It explains that primary structure refers to the amino acid sequence of a protein. Secondary structure describes local folding patterns like alpha helices and beta sheets. Tertiary structure is the overall 3D shape of a single protein chain that results from folding. Quaternary structure involves the shape and interactions of multiple protein subunits. The document provides examples and diagrams to illustrate each level of structure and how they relate to determining a protein's function.
This document discusses various topics relating to protein structure and bioinformatics. It begins with an overview of protein structure and why understanding protein structure is important. It then discusses the different levels of protein structure from primary to quaternary structure. Methods for determining protein structure like X-ray crystallography and NMR are mentioned. Databases for storing protein structures like the Protein Data Bank are also summarized. The document touches on topics like protein folding, domains, membrane protein topology, and secondary structure prediction methods.
This document discusses protein structure and bioinformatics. It begins by explaining the rationale for understanding protein structure and function, including determining protein sequences, structures, and relating this to function. It then covers levels of protein structure from primary to quaternary, methods for determining protein structures like X-ray crystallography, and uses of protein modeling and databases. The document provides examples of protein domains, folds, and membrane protein topology. It emphasizes that sequence determines conformation and that structure implies function.
This document discusses various methods for predicting genes and analyzing unknown DNA sequences, including:
- Using profiles, patterns, and hidden Markov models (HMMs) to find conserved sequences and predict protein function
- Ontologies like Gene Ontology that organize genes and gene products in a structured network to facilitate annotation and analysis
- Computational tools like Genefinder and Glimmer that use signals like coding potential, open reading frames, start/stop codons, and sequence similarity to known genes to predict gene structures in sequences
- Integrating multiple lines of evidence, like HMMs, EST alignments, repeats, and CpG islands, can improve gene prediction over a single method.
The document discusses various bioinformatics tools and concepts. It introduces GitHub as a code hosting platform and describes control structures, lists, and dictionaries in Python. It also covers topics like regular expressions, Biopython, parsing sequences from online databases, secondary structure prediction using Chou-Fasman algorithm, and transmembrane region prediction using Kyte-Doolittle hydropathy plots.
The document discusses hosting a GIT repository on GitHub and accepting an invitation to a shared repository called "Bioinformatics-I-2015" hosted on GitHub.ugent.be. It also lists some example control structures for Python scripts like if/else statements and for loops and poses extra bioinformatics analysis questions that could be answered using Python scripts.
This study investigated the role of the amino and carboxyl terminal regions of cytosolic serine hydroxymethyltransferase (SHMT) in subunit assembly and catalysis. Six N-terminal and two C-terminal deletion mutants were constructed from a full-length SHMT cDNA clone and expressed in E. coli. The two shortest N-terminal deletion mutants (lacking the first 6 and 14 residues) were purified and found to be catalytically active, but the 14-residue mutant had decreased thermal stability compared to the full enzyme. The 14-residue mutant also predominantly formed dimers rather than tetramers and had reduced ability to reconstitute activity after removal of the cofactor. These results demonstrate that the N-terminal region plays
This study used in silico and in vitro methods to characterize the unknown protein 4EZI from the Protein Data Bank. In silico screening with ProMOL, BLAST, and DALI suggested 4EZI was part of the esterase family. This was confirmed experimentally by expressing and purifying 4EZI, and using activity assays that showed 4EZI could hydrolyze short-chain but not long-chain p-nitrophenyl esters, characteristic of an esterase rather than a lipase. Therefore, through integrated computational and experimental analysis, 4EZI was determined to be an esterase.
The document describes the design and synthesis of tweezer peptide mimics of the estrogen receptor for screening endocrine disrupting chemicals. Key points:
- The project aims to design an artificial receptor to mimic the hormone binding domain of the estrogen receptor (ER) in order to screen for endocrine disrupting chemicals (EDCs).
- Three types of tweezer peptide mimics (Types A, B, and C) were designed based on selected amino acids known to interact with EDCs in the binding pocket of the natural ER.
- Type A mimics were synthesized using linear solid phase peptide synthesis. Types B and C used circular solid phase peptide synthesis and a combination of linear and click chemistry
This document discusses various topics relating to protein structure and bioinformatics. It begins with an overview of protein structure and why understanding protein structure is important. It then discusses the different levels of protein structure from primary to quaternary structure. Methods for determining protein structure like X-ray crystallography and NMR are mentioned. Databases for storing protein structures like the Protein Data Bank are also summarized. The document touches on topics like protein folding, domains, membrane protein topology, and secondary structure prediction methods.
This document discusses protein structure and bioinformatics. It begins by explaining the rationale for understanding protein structure and function, including determining protein sequences, structures, and relating this to function. It then covers levels of protein structure from primary to quaternary, methods for determining protein structures like X-ray crystallography, and uses of protein modeling and databases. The document provides examples of protein domains, folds, and membrane protein topology. It emphasizes that sequence determines conformation and that structure implies function.
This document discusses various methods for predicting genes and analyzing unknown DNA sequences, including:
- Using profiles, patterns, and hidden Markov models (HMMs) to find conserved sequences and predict protein function
- Ontologies like Gene Ontology that organize genes and gene products in a structured network to facilitate annotation and analysis
- Computational tools like Genefinder and Glimmer that use signals like coding potential, open reading frames, start/stop codons, and sequence similarity to known genes to predict gene structures in sequences
- Integrating multiple lines of evidence, like HMMs, EST alignments, repeats, and CpG islands, can improve gene prediction over a single method.
The document discusses various bioinformatics tools and concepts. It introduces GitHub as a code hosting platform and describes control structures, lists, and dictionaries in Python. It also covers topics like regular expressions, Biopython, parsing sequences from online databases, secondary structure prediction using Chou-Fasman algorithm, and transmembrane region prediction using Kyte-Doolittle hydropathy plots.
The document discusses hosting a GIT repository on GitHub and accepting an invitation to a shared repository called "Bioinformatics-I-2015" hosted on GitHub.ugent.be. It also lists some example control structures for Python scripts like if/else statements and for loops and poses extra bioinformatics analysis questions that could be answered using Python scripts.
This study investigated the role of the amino and carboxyl terminal regions of cytosolic serine hydroxymethyltransferase (SHMT) in subunit assembly and catalysis. Six N-terminal and two C-terminal deletion mutants were constructed from a full-length SHMT cDNA clone and expressed in E. coli. The two shortest N-terminal deletion mutants (lacking the first 6 and 14 residues) were purified and found to be catalytically active, but the 14-residue mutant had decreased thermal stability compared to the full enzyme. The 14-residue mutant also predominantly formed dimers rather than tetramers and had reduced ability to reconstitute activity after removal of the cofactor. These results demonstrate that the N-terminal region plays
This study used in silico and in vitro methods to characterize the unknown protein 4EZI from the Protein Data Bank. In silico screening with ProMOL, BLAST, and DALI suggested 4EZI was part of the esterase family. This was confirmed experimentally by expressing and purifying 4EZI, and using activity assays that showed 4EZI could hydrolyze short-chain but not long-chain p-nitrophenyl esters, characteristic of an esterase rather than a lipase. Therefore, through integrated computational and experimental analysis, 4EZI was determined to be an esterase.
The document describes the design and synthesis of tweezer peptide mimics of the estrogen receptor for screening endocrine disrupting chemicals. Key points:
- The project aims to design an artificial receptor to mimic the hormone binding domain of the estrogen receptor (ER) in order to screen for endocrine disrupting chemicals (EDCs).
- Three types of tweezer peptide mimics (Types A, B, and C) were designed based on selected amino acids known to interact with EDCs in the binding pocket of the natural ER.
- Type A mimics were synthesized using linear solid phase peptide synthesis. Types B and C used circular solid phase peptide synthesis and a combination of linear and click chemistry
Structure-Function Analysis of POR MutantsAYang999
This document summarizes protein structure and function, with a focus on mutations in the protein Cytochrome P450 Reductase (POR). It describes how: (1) POR's amino acid sequence determines its primary structure and folding; (2) genetic mutations can alter structure and compromise function; and (3) specific POR mutations are associated with Antley-Bixler Syndrome. The document outlines experiments to characterize the S102P and R550Q POR mutants using protein expression, purification, and a cytochrome c reduction assay to analyze structural and functional effects compared to wild type POR.
Structure-Function Analysis of POR MutantsAYang999
This document summarizes protein structure and function, with a focus on mutations in the protein cytochrome P450 oxidoreductase (POR). It describes how mutations can alter a protein's amino acid sequence and structure. Specifically, it investigates the S102P and R550Q mutations in POR, which were found in humans. Experiments expressed and purified these mutant POR proteins, and will test their electron transfer activity compared to the wild type protein using a cytochrome c reduction assay. This will help determine if these mutations impair POR function.
The document discusses protein structure and its importance in determining protein function. It covers several key points:
1) There are multiple levels of protein structure from primary to quaternary structure. Higher-order structures like tertiary structure bring distant parts of the amino acid sequence into proximity, allowing proteins to perform their functions.
2) Protein structure is determined by the amino acid sequence through the physical properties of residues. The sequence encodes the folding pathway that results in a stable, functional 3D structure.
3) Experimental methods like X-ray crystallography and NMR spectroscopy are used to determine high-resolution protein structures that reveal how structure enables function. Databases like PDB archive and classify protein structures.
Impact of Bem1p Mutant Alleles on [PSI+] Prion FormationMizuki Kato
The document describes a study investigating the impact of Bem1p mutant alleles on the formation of the [PSI+] prion in yeast. Bem1p is a scaffolding protein involved in cell polarization and actin organization. Certain Bem1p mutants that affect protein binding are predicted to lower the frequency of [PSI+] formation by disrupting actin-dependent transport of prion aggregates. The study aims to transform yeast strains with plasmids expressing a prion domain or Bem1p mutants, induce prion formation, and determine the impact on frequency. Problems were encountered with some transformations that require troubleshooting.
This document discusses intrinsically disordered proteins (IDPs), which lack a fixed three-dimensional structure under physiological conditions and instead exist as dynamic ensembles. It notes that IDPs challenge the traditional view that proteins require a well-defined structure to function. The document also mentions that IDPs often gain structure upon binding to their protein partners, and that their flexible, disordered state allows for low affinity but high specificity interactions optimal for regulation. Finally, it suggests intrinsic disorder may have evolved to allow for extended interaction surfaces and efficient signal processing.
Enzyme Discovery for Natural Product BiosynthesisHongnan Cao
A poster presentation of collaborative work on the NIH funded project of Enzyme Discovery for Natural Product Biosynthesis at 2015 American Crystallography Association Meeting at Philadelphia, PA. Thanks to Rice University, University of Wisconsin-Madison, The Scripps Research Institute, University of Kentucky, The Midwest Center for Structural Genomics, The Northeast Center for Structural Genomics, APS synchrotron at Argonne National Lab
The document describes several classes of molecular markers used in genetic analysis, including isozymes, RFLPs, RAPDs, AFLPs, microsatellites, and SNPs. Isozymes analyze differences in protein mobility on a gel, while RFLPs, RAPDs, AFLPs detect DNA fragment length polymorphisms. Microsatellites analyze differences in repeat number, and SNPs detect single nucleotide differences. Each method has advantages and disadvantages related to factors like technical requirements, costs, reproducibility, and amount of polymorphism detected. The choice of marker depends on the application and study objectives.
This document discusses DNA structure and organization within cells. It describes how DNA is organized into genes that encode proteins, and how multiple genes together make up an organism's genome. DNA is packaged into chromosomes, which can further condense during cell division. The document outlines how DNA is wrapped around histone proteins to form nucleosomes, which allow for tight packing of DNA into chromatin and chromosomes. Post-translational modifications of histone proteins can affect chromatin structure and regulate gene expression.
BT631-12-X-ray_crystallography_protein_crystallizationRajesh G
Crystallizing proteins involves obtaining pure protein, determining initial crystallization conditions through trials, and optimizing crystals for diffraction analysis. Key factors that affect crystallization include protein purity and concentration, pH, temperature, buffers, and precipitation techniques such as vapor diffusion. Lysozyme is commonly used to optimize crystallization methods due to its low cost and ease of obtaining crystals under various conditions including salts or polymers. The goal is to produce well-diffracting crystals to determine the 3D protein structure.
This document provides an overview of nanotechnology, including definitions, history, applications, and health impacts. Nanotechnology involves engineering at the molecular level between 1 to 100 nanometers. It has a variety of applications, including carbon nanotubes, molecular electronics, quantum dots, and more efficient energy generation. While many nanotechnology applications pose no new health risks, some free nanoparticles may have negative health impacts due to their small size and chemical properties. The document outlines the history and development of nanotechnology from 1959 to present.
Nanotechnology is the purposeful manipulation of matter on an atomic scale. Materials created in this manner often exhibit unique physical and chemical properties, which have useful applications in various industries. A growing use for some types of engineered nanomaterials is in the area of environmental remediation, termed nanoremediation. While this technique appears to be effective for cleanup, there are still many unanswered questions regarding its long-term impact to environmental quality and human health. No long-term studies exist regarding the potential environmental impact of nanoremediation. While animal studies have shown the potential for adverse health effects, limited data regarding human health are available. The US Environmental Protection Agency is currently adapting existing regulations to cover the use of nanomaterials in remediation, but this approach is limited. Many questions still remain regarding fate and transport, verification of clean-up, and potential occupational and community exposures.
This document provides an overview of nanotechnology including definitions, history, and applications. It defines nanotechnology as the design and manipulation of materials at the nanoscale (1-100 nm) to produce novel properties. Nanomaterials are characterized by their small size and increased surface area. The document outlines the bottom-up and top-down approaches to nanotechnology and gives examples of applications in various fields including medicine, computing, and the environment. It specifically discusses applications of nanotechnology for water purification such as detection, filtration membranes, and biofilm removal. The document concludes with noting both the promise and environmental implications of nanotechnology that require further study.
nanotechnology has entered the sphere of water treatment processes. Many different types of nanomaterial’s are being evaluated and also being used in water treatment process.
Desalination is a key market area. Vast majority of worlds water is salt water, and though technology has existed for years that enables the desalination of ocean water, it is often a very energy intensive procedure and therefore expensive
X-ray crystallography allows us to determine protein structures at the atomic level by visualizing the electron density map generated from X-ray diffraction data of protein crystals. Several steps are involved including growing high quality protein crystals, mounting crystals in the X-ray beam to collect diffraction data, solving the phase problem to produce an electron density map, building and refining an atomic model that fits the map, and validating the final protein structure. This technique provides insights into protein function and enables structure-based drug design.
This document contains an agenda for bioinformatics lessons covering various topics like biological databases, sequence similarity, sequence alignments, database searching, phylogenetics, protein structure, gene prediction, and bioinformatics applications in drug discovery. It also discusses ongoing bioinformatics research projects and ambitions to publish peer-reviewed work. Finally, it provides background on protein structure, levels of protein structure from primary to tertiary, and experimental methods like X-ray crystallography used to determine protein structures.
The document discusses various topics in bioinformatics and protein structure. It provides an overview of ongoing thesis topics at Biobix including biomarker prediction, methylation, metabolomics, peptidomics, and more. It also discusses the rationale for understanding protein structure and function, levels of protein structure from primary to quaternary, methods for determining structure like X-ray crystallography, and approaches to secondary structure prediction including Chou-Fasman.
Proteins are macromolecules built from amino acids that are classified in many ways, including by their structure and function. There are four levels of protein structure: primary, secondary, tertiary, and quaternary. The primary structure is the amino acid sequence, which determines the local 3D secondary structures like alpha helices and beta sheets through hydrogen bonding. The tertiary structure describes the overall 3D shape formed by interactions between secondary structures. Some proteins are made of multiple subunits, forming the quaternary structure through noncovalent bonds between subunits. Determining protein structures involves purifying the protein and using techniques like amino acid sequencing, fragmentation, and X-ray crystallography.
The document discusses several topics related to protein structure prediction using Python:
1. It introduces the Chou-Fasman algorithm for predicting protein secondary structure from amino acid sequence. The algorithm calculates preference parameters for each amino acid to be in alpha helices, beta sheets, or other structures.
2. It provides an example of calculating helical propensity.
3. It lists the preference parameters output by the Chou-Fasman algorithm for each amino acid.
4. It outlines the steps of applying the Chou-Fasman algorithm to predict secondary structure elements in a protein sequence.
Computational Prediction Of Protein-1.pptxashharnomani
This document discusses computational methods for predicting protein structure, including homology modeling, fold recognition/threading, and ab initio prediction. Homology modeling predicts structure based on sequence similarity to proteins with known structures. It involves aligning the target sequence to template structures, then modeling secondary structure, loops, and side chains. Accuracy depends on template quality and sequence identity above 30%. Fold recognition matches sequences to structure folds without clear homology. Ab initio prediction predicts structure from sequence alone using physics-based forces.
Cross Product Extensions to the Gene OntologyChris Mungall
The document discusses extending the Gene Ontology (GO) through assigning logical computable definitions to GO classes. This involves partitioning GO classes into "cross product" sets based on the ontologies used in the definitions. Over 13,000 GO classes now have provisional logical definitions assigned using this approach, covering molecular function, biological process, cellular component, and other ontologies. The logical definitions allow for nested descriptions and reasoning over GO classes. Anatomy classes are standardized in the Uberon cross-species anatomy ontology.
Structure-Function Analysis of POR MutantsAYang999
This document summarizes protein structure and function, with a focus on mutations in the protein Cytochrome P450 Reductase (POR). It describes how: (1) POR's amino acid sequence determines its primary structure and folding; (2) genetic mutations can alter structure and compromise function; and (3) specific POR mutations are associated with Antley-Bixler Syndrome. The document outlines experiments to characterize the S102P and R550Q POR mutants using protein expression, purification, and a cytochrome c reduction assay to analyze structural and functional effects compared to wild type POR.
Structure-Function Analysis of POR MutantsAYang999
This document summarizes protein structure and function, with a focus on mutations in the protein cytochrome P450 oxidoreductase (POR). It describes how mutations can alter a protein's amino acid sequence and structure. Specifically, it investigates the S102P and R550Q mutations in POR, which were found in humans. Experiments expressed and purified these mutant POR proteins, and will test their electron transfer activity compared to the wild type protein using a cytochrome c reduction assay. This will help determine if these mutations impair POR function.
The document discusses protein structure and its importance in determining protein function. It covers several key points:
1) There are multiple levels of protein structure from primary to quaternary structure. Higher-order structures like tertiary structure bring distant parts of the amino acid sequence into proximity, allowing proteins to perform their functions.
2) Protein structure is determined by the amino acid sequence through the physical properties of residues. The sequence encodes the folding pathway that results in a stable, functional 3D structure.
3) Experimental methods like X-ray crystallography and NMR spectroscopy are used to determine high-resolution protein structures that reveal how structure enables function. Databases like PDB archive and classify protein structures.
Impact of Bem1p Mutant Alleles on [PSI+] Prion FormationMizuki Kato
The document describes a study investigating the impact of Bem1p mutant alleles on the formation of the [PSI+] prion in yeast. Bem1p is a scaffolding protein involved in cell polarization and actin organization. Certain Bem1p mutants that affect protein binding are predicted to lower the frequency of [PSI+] formation by disrupting actin-dependent transport of prion aggregates. The study aims to transform yeast strains with plasmids expressing a prion domain or Bem1p mutants, induce prion formation, and determine the impact on frequency. Problems were encountered with some transformations that require troubleshooting.
This document discusses intrinsically disordered proteins (IDPs), which lack a fixed three-dimensional structure under physiological conditions and instead exist as dynamic ensembles. It notes that IDPs challenge the traditional view that proteins require a well-defined structure to function. The document also mentions that IDPs often gain structure upon binding to their protein partners, and that their flexible, disordered state allows for low affinity but high specificity interactions optimal for regulation. Finally, it suggests intrinsic disorder may have evolved to allow for extended interaction surfaces and efficient signal processing.
Enzyme Discovery for Natural Product BiosynthesisHongnan Cao
A poster presentation of collaborative work on the NIH funded project of Enzyme Discovery for Natural Product Biosynthesis at 2015 American Crystallography Association Meeting at Philadelphia, PA. Thanks to Rice University, University of Wisconsin-Madison, The Scripps Research Institute, University of Kentucky, The Midwest Center for Structural Genomics, The Northeast Center for Structural Genomics, APS synchrotron at Argonne National Lab
The document describes several classes of molecular markers used in genetic analysis, including isozymes, RFLPs, RAPDs, AFLPs, microsatellites, and SNPs. Isozymes analyze differences in protein mobility on a gel, while RFLPs, RAPDs, AFLPs detect DNA fragment length polymorphisms. Microsatellites analyze differences in repeat number, and SNPs detect single nucleotide differences. Each method has advantages and disadvantages related to factors like technical requirements, costs, reproducibility, and amount of polymorphism detected. The choice of marker depends on the application and study objectives.
This document discusses DNA structure and organization within cells. It describes how DNA is organized into genes that encode proteins, and how multiple genes together make up an organism's genome. DNA is packaged into chromosomes, which can further condense during cell division. The document outlines how DNA is wrapped around histone proteins to form nucleosomes, which allow for tight packing of DNA into chromatin and chromosomes. Post-translational modifications of histone proteins can affect chromatin structure and regulate gene expression.
BT631-12-X-ray_crystallography_protein_crystallizationRajesh G
Crystallizing proteins involves obtaining pure protein, determining initial crystallization conditions through trials, and optimizing crystals for diffraction analysis. Key factors that affect crystallization include protein purity and concentration, pH, temperature, buffers, and precipitation techniques such as vapor diffusion. Lysozyme is commonly used to optimize crystallization methods due to its low cost and ease of obtaining crystals under various conditions including salts or polymers. The goal is to produce well-diffracting crystals to determine the 3D protein structure.
This document provides an overview of nanotechnology, including definitions, history, applications, and health impacts. Nanotechnology involves engineering at the molecular level between 1 to 100 nanometers. It has a variety of applications, including carbon nanotubes, molecular electronics, quantum dots, and more efficient energy generation. While many nanotechnology applications pose no new health risks, some free nanoparticles may have negative health impacts due to their small size and chemical properties. The document outlines the history and development of nanotechnology from 1959 to present.
Nanotechnology is the purposeful manipulation of matter on an atomic scale. Materials created in this manner often exhibit unique physical and chemical properties, which have useful applications in various industries. A growing use for some types of engineered nanomaterials is in the area of environmental remediation, termed nanoremediation. While this technique appears to be effective for cleanup, there are still many unanswered questions regarding its long-term impact to environmental quality and human health. No long-term studies exist regarding the potential environmental impact of nanoremediation. While animal studies have shown the potential for adverse health effects, limited data regarding human health are available. The US Environmental Protection Agency is currently adapting existing regulations to cover the use of nanomaterials in remediation, but this approach is limited. Many questions still remain regarding fate and transport, verification of clean-up, and potential occupational and community exposures.
This document provides an overview of nanotechnology including definitions, history, and applications. It defines nanotechnology as the design and manipulation of materials at the nanoscale (1-100 nm) to produce novel properties. Nanomaterials are characterized by their small size and increased surface area. The document outlines the bottom-up and top-down approaches to nanotechnology and gives examples of applications in various fields including medicine, computing, and the environment. It specifically discusses applications of nanotechnology for water purification such as detection, filtration membranes, and biofilm removal. The document concludes with noting both the promise and environmental implications of nanotechnology that require further study.
nanotechnology has entered the sphere of water treatment processes. Many different types of nanomaterial’s are being evaluated and also being used in water treatment process.
Desalination is a key market area. Vast majority of worlds water is salt water, and though technology has existed for years that enables the desalination of ocean water, it is often a very energy intensive procedure and therefore expensive
X-ray crystallography allows us to determine protein structures at the atomic level by visualizing the electron density map generated from X-ray diffraction data of protein crystals. Several steps are involved including growing high quality protein crystals, mounting crystals in the X-ray beam to collect diffraction data, solving the phase problem to produce an electron density map, building and refining an atomic model that fits the map, and validating the final protein structure. This technique provides insights into protein function and enables structure-based drug design.
This document contains an agenda for bioinformatics lessons covering various topics like biological databases, sequence similarity, sequence alignments, database searching, phylogenetics, protein structure, gene prediction, and bioinformatics applications in drug discovery. It also discusses ongoing bioinformatics research projects and ambitions to publish peer-reviewed work. Finally, it provides background on protein structure, levels of protein structure from primary to tertiary, and experimental methods like X-ray crystallography used to determine protein structures.
The document discusses various topics in bioinformatics and protein structure. It provides an overview of ongoing thesis topics at Biobix including biomarker prediction, methylation, metabolomics, peptidomics, and more. It also discusses the rationale for understanding protein structure and function, levels of protein structure from primary to quaternary, methods for determining structure like X-ray crystallography, and approaches to secondary structure prediction including Chou-Fasman.
Proteins are macromolecules built from amino acids that are classified in many ways, including by their structure and function. There are four levels of protein structure: primary, secondary, tertiary, and quaternary. The primary structure is the amino acid sequence, which determines the local 3D secondary structures like alpha helices and beta sheets through hydrogen bonding. The tertiary structure describes the overall 3D shape formed by interactions between secondary structures. Some proteins are made of multiple subunits, forming the quaternary structure through noncovalent bonds between subunits. Determining protein structures involves purifying the protein and using techniques like amino acid sequencing, fragmentation, and X-ray crystallography.
The document discusses several topics related to protein structure prediction using Python:
1. It introduces the Chou-Fasman algorithm for predicting protein secondary structure from amino acid sequence. The algorithm calculates preference parameters for each amino acid to be in alpha helices, beta sheets, or other structures.
2. It provides an example of calculating helical propensity.
3. It lists the preference parameters output by the Chou-Fasman algorithm for each amino acid.
4. It outlines the steps of applying the Chou-Fasman algorithm to predict secondary structure elements in a protein sequence.
Computational Prediction Of Protein-1.pptxashharnomani
This document discusses computational methods for predicting protein structure, including homology modeling, fold recognition/threading, and ab initio prediction. Homology modeling predicts structure based on sequence similarity to proteins with known structures. It involves aligning the target sequence to template structures, then modeling secondary structure, loops, and side chains. Accuracy depends on template quality and sequence identity above 30%. Fold recognition matches sequences to structure folds without clear homology. Ab initio prediction predicts structure from sequence alone using physics-based forces.
Cross Product Extensions to the Gene OntologyChris Mungall
The document discusses extending the Gene Ontology (GO) through assigning logical computable definitions to GO classes. This involves partitioning GO classes into "cross product" sets based on the ontologies used in the definitions. Over 13,000 GO classes now have provisional logical definitions assigned using this approach, covering molecular function, biological process, cellular component, and other ontologies. The logical definitions allow for nested descriptions and reasoning over GO classes. Anatomy classes are standardized in the Uberon cross-species anatomy ontology.
Structure, functions and folding problems of proteinRawat DA Greatt
This document provides an overview of protein structure and folding. It discusses the four levels of protein structure - primary, secondary, tertiary, and quaternary. Common secondary structures like alpha helices and beta sheets are described. The document also introduces concepts like the Ramachandran plot, which maps allowed phi and psi dihedral angles in protein backbones. Protein folding and factors involved like molecular chaperones are also summarized. Disorders resulting from changes in protein conformation are briefly mentioned.
Protein folding is the process by which a protein goes from an unfolded state to its biologically active three-dimensional structure. It is important to understand protein folding to help predict protein structures from sequence alone and to understand diseases caused by protein misfolding. Proteins typically fold through progressive formation of native-like structures rather than through a random search. Molecular chaperones help other proteins fold within cells. Misfolded proteins can form amyloid fibrils associated with diseases. Computational methods aim to predict protein structures from sequence using fragment libraries and modeling protein energy landscapes. Protein design techniques aim to computationally modify protein sequences to achieve desired stabilities, functions, and binding properties.
The document discusses various bioinformatics tools and algorithms for analyzing protein sequences, including Biopython for working with biological sequence data, the Kyte-Doolittle algorithm for predicting transmembrane regions, and the Chou-Fasman algorithm for predicting secondary structure from amino acid preferences for alpha helices, beta sheets, and random coils. It also provides examples of analyzing Swiss-Prot data to find properties of human proteins and applying these tools and libraries to extract insights from protein sequences.
Protein Structural Prediction
1. Molecular Structure prediction
2. Sequence
3. Protein Folding
4. The Leventhal Paradox
5. Energy (Minimization )
6. The Hydrophobic Effect
7. Protein Structure Determination ( X-ray,NMR)
8. Ab initio Prediction
9. Lattice String Folding
10. Rosetta (Monte Carlo based method)
11. Homology-based Prediction
lehninger(sixth edition) Ch 03: Amino acids, peptides and proteinskrupal parmar
1. The document discusses various methods for purifying proteins, including ammonium sulfate fractionation, dialysis, column chromatography techniques like ion exchange and size exclusion, and electrophoresis methods like SDS-PAGE and 2D gels.
2. Key steps in protein purification include assaying fractions to determine total protein and specific activity in order to calculate purification fold. Purity can be evaluated using electrophoresis.
3. Molecular techniques for protein purification involve expressing a recombinant fusion protein, lysing cells, and using affinity chromatography based on the fusion tag to purify the protein before cleaving off the tag.
This document provides information about genome annotation. It begins by describing how open reading frames (ORFs) are identified in genomes and how genomes are annotated. It discusses the types of databases used to classify genes, such as those involved in metabolism. It provides examples of how genes are categorized, including by enzyme commission numbers, FIGfams, Pfam, COGs, KEGG Orthology numbers, and metabolic pathways. It also discusses topics like pseudogenes, the origin of replication, ribosomal operons, GC skew, and central carbon metabolism pathways like glycolysis and the Entner-Doudoroff pathway.
PomBase conventions for improving annotation depth, breadth, consistency and ...Valerie Wood
PomBase uses a combination of annotation conventions and QC mechanisms. In addition to identifying annotation inconsistencies and errors, these combined methods improve information content, annotation coverage, depth or specificity and redundancy.
The document discusses protein structure prediction. It describes that a protein's amino acid sequence determines its 3-dimensional structure, which in turn determines its function. There are four levels of protein structure: primary, secondary, tertiary, and quaternary. Computational methods for predicting structure include homology modeling, which predicts structure based on similarity to proteins with known structures, and ab initio modeling, which predicts structure directly from physical principles. Current ab initio methods struggle with the vast number of possible protein conformations.
The document discusses using proteomics to develop vaccines. It describes how proteomics can help understand protein interactions for vaccine development. The document then focuses on developing a vaccine for Lassa fever. It outlines computational methods used to analyze the Lassa virus glycoprotein, including determining its structure, domains, and interactions within cells. The goal is to use this analysis to develop a stabilized vaccine candidate against Lassa virus that can protect humans.
This document discusses various methods for predicting protein function from sequence and structure. It begins by explaining the importance of predicting protein function for applications like disease diagnosis and drug discovery. It then outlines different types of data that can be used for functional prediction, including sequence, structure, expression profiles, and interactions. Both sequence-based methods like homology searches and domain identification as well as structure-based approaches are covered. Specific tools discussed include BLAST, Pfam, SCOP, CATH, and ProFunc. The document emphasizes that functional prediction is challenging given proteins can have multiple functions and homology does not always imply similar function. It also notes limitations of simple homology searches.
This document provides an overview of protein structure from primary to quaternary levels. It discusses the building blocks of proteins including amino acids and peptide bonds. Secondary structures like alpha helices and beta sheets are explained. Tertiary structure refers to the global folding of the protein chain. Quaternary structure involves the assembly of multiple protein subunits. Examples are given of protein complexes demonstrating tertiary and quaternary levels of structure. The document also outlines different classes of proteins based on function, structure, and cellular localization.
- Proteomics involves the analysis of proteins, including isolation, separation, and identification techniques
- Key separation methods are SDS-PAGE, which separates by size, and 2D gel electrophoresis, which separates by both size and isoelectric point
- Protein identification relies on mass spectrometry to determine the mass-to-charge ratio of protein fragments, which are then matched to theoretical fragment masses to identify the source protein
The document discusses protein-protein interactions (PPIs) and methods used to study them. It defines PPIs as physical contacts between two or more proteins through biochemical or electrostatic forces. It describes different types of PPIs including homo-oligomers, hetero-oligomers, covalent and non-covalent interactions. Common methods to study PPIs are also summarized, such as yeast two-hybrid systems, co-immunoprecipitation, and protein interaction databases. The applications and importance of PPI research are mentioned including roles in various cellular processes and diseases.
This document provides an overview of basic molecular biology concepts including:
1) DNA structure including nucleotides, base pairing, and the double helix formation.
2) Genes and genomes, including definitions of a gene, genome size comparisons, and that genes encode proteins.
3) The genetic code and mutations, including how the DNA sequence is translated into proteins and different types of mutations.
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6. BPC 2015
*** ERGRO *** 1. Longest English word where first three
letters are identical to the last three
2. English word where longest stretch of letters
are identical at beginning and at the end
3. In Dutch ?
4. Any other language
5. Biological relevance ?
Send before 1st of december to
wim.vancriekinge@gmail.com
Longest one wins, if same size first to submit
7.
8.
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11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28. The reason for “bioinformatics” to exist ?
• empirical finding: if two biological
sequences are sufficiently similar, almost
invariably they have similar biological
functions and will be descended from a
common ancestor.
• (i) function is encoded into sequence,
this means: the sequence provides the
syntax and
• (ii) there is a redundancy in the
encoding, many positions in the
sequence may be changed without
perceptible changes in the function, thus
the semantics of the encoding is robust.
29. Protein Structure
Introduction
Why ?
How do proteins fold ?
Levels of protein structure
0,1,2,3,4
X-ray / NMR
The Protein Database (PDB)
Protein Modeling
Bioinformatics & Proteomics
Weblems
30. • Proteins perform a variety of cellular
tasks in the living cells
• Each protein adopts a particular folding
that determines its function
• The 3D structure of a protein can bring
into close proximity residues that are far
apart in the amino acid sequence
• Catalytic site: Business End of the
molecule
Why protein structure ?
31. Rationale for understanding protein structure and function
Protein sequence
-large numbers of
sequences, including
whole genomes
Protein function
- rational drug design and treatment of disease
- protein and genetic engineering
- build networks to model cellular pathways
- study organismal function and evolution
?
structure determination
structure prediction
homology
rational mutagenesis
biochemical analysis
model studies
Protein structure
- three dimensional
- complicated
- mediates function
32. About the use of protein models (Peitch)
• Structure is preserved under evolution when
sequence is not
– Interpreting the impact of mutations/SNPs and conserved
residues on protein function. Potential link to disease
• Function ?
– Biochemical: the chemical interactions occerring in a protein
– Biological: role within the cell
– Phenotypic: the role in the organism
• Gene Ontology functional classification !
– Priorisation of residues to mutate to determine protein
function
– Providing hints for protein function:Catalytic mechanisms
of enzymes often require key residues to be close
together in 3D space
– (protein-ligand complexes, rational drug design, putative
interaction interfaces)
33. MIS-SENSE MUTATION
e.g. Sickle Cell Anaemia
Cause: defective haemoglobin due to mutation in β-
globin gene
Symptoms: severe anaemia and death in homozygote
34. Normal β-globin - 146 amino acids
val - his - leu - thr - pro - glu - glu - ---------
1 2 3 4 5 6 7
Normal gene (aa 6) Mutant gene
DNA CTC CAC
mRNA GAG GUG
Product Glu Valine
Mutant β-globin
val - his - leu - thr - pro - val - glu - ---------
35. Protein Conformation
• Christian Anfinsen
Studies on reversible denaturation
“Sequence specifies conformation”
• Chaperones and disulfide
interchange enzymes:
involved but not controlling final state, they
provide environment to refold if misfolded
• Structure implies function: The amino
acid sequence encodes the protein’s
structural information
36. • by itself:
– Anfinsen had developed what he called his
"thermodynamic hypothesis" of protein folding to explain
the native conformation of amino acid structures. He
theorized that the native or natural conformation occurs
because this particular shape is thermodynamically the
most stable in the intracellular environment. That is, it
takes this shape as a result of the constraints of the
peptide bonds as modified by the other chemical and
physical properties of the amino acids.
– To test this hypothesis, Anfinsen unfolded the RNase
enzyme under extreme chemical conditions and observed
that the enzyme's amino acid structure refolded
spontaneously back into its original form when he returned
the chemical environment to natural cellular conditions.
– "The native conformation is determined by the totality of
interatomic interactions and hence by the amino acid
sequence, in a given environment."
How does a protein fold ?
37. Protein Structure
Introduction
Why ?
How do proteins fold ?
Levels of protein structure
0,1,2,3,4
X-ray / NMR
The Protein Database (PDB)
Protein Modeling
Bioinformatics & Proteomics
Weblems
38. • Proteins are linear heteropolymers: one or more
polypeptide chains
• Below about 40 residues the term peptide is frequently
used.
• A certain number of residues is necessary to perform a
particular biochemical function, and around 40-50
residues appears to be the lower limit for a functional
domain size.
• Protein sizes range from this lower limit to several
hundred residues in multi-functional proteins.
• Three-dimentional shapes (folds) adopted vary
enormously
• Experimental methods:
– X-ray crystallography
– NMR (nuclear magnetic resonance)
– Electron microscopy
– Ab initio calculations …
The Basics
39. • Zeroth: amino acid composition
(proteomics, %cysteine, %glycine)
Levels of protein structure
40. The basic structure of an a-amino acid is quite simple. R denotes any one of the
20 possible side chains (see table below). We notice that the Ca-atom has 4
different ligands (the H is omitted in the drawing) and is thus chiral. An easy
trick to remember the correct L-form is the CORN-rule: when the Ca-atom is
viewed with the H in front, the residues read "CO-R-N" in a clockwise
direction.
Amino Acid Residues
49. • Secondary
– Local organization of the protein backbone: alpha-
helix, Beta-strand (which assemble into Beta-
sheets) turn and interconnecting loop.
Levels of protein structure
52. • Residues with hydrophobic properties
conserved at i, i+2, i+4 separated by
unconserved or hydrophilic residues
suggest surface beta- strands.
A short run of hydrophobic amino acids
(4 residues) suggests a buried beta-
strand.
Pairs of conserved hydrophobic amino
acids separated by pairs of
unconserved, or hydrophilic residues
suggests an alfa-helix with one face
packing in the protein core. Likewise,
an i, i+3, i+4, i+7 pattern of conserved
hydrophobic residues.
A Practical Approach: Interpretation
56. • Chou, P.Y. and Fasman, G.D. (1974).
Conformational parameters for amino acids in helical, b-
sheet, and random coil regions calculated from proteins.
Biochemistry 13, 211-221.
• Chou, P.Y. and Fasman, G.D. (1974).
Prediction of protein conformation.
Biochemistry 13, 222-245.
Secondary structure prediction:CHOU-FASMAN
57. •Method
•Assigning a set of prediction values to a
residue, based on statistic analysis of 15
proteins
• Applying a simple algorithm to those
numbers
Secondary structure prediction:CHOU-FASMAN
58. Calculation of preference parameters
observed counts
• P = Log --------------------- + 1.0
expected counts
• Preference parameter > 1.0 specific residue has a
preference for the specific secondary structure.
• Preference parameter = 1.0 specific residue does not
have a preference for, nor dislikes the specific secondary
structure.
• Preference parameter < 1.0 specific residue dislikes the
specific secondary structure.
For each of the 20 residues and each secondary structure (a-
helix, b-sheet and b-turn):
Secondary structure prediction:CHOU-FASMAN
60. Applying algorithm
1. Assign parameters to residue.
2. Identify regions where 4 out of 6 residues have P(a)>100: a-helix. Extend
helix in both directions until four contiguous residues have an average
P(a)<100: end of a-helix. If segment is longer than 5 residues and P(a)>P(b):
a-helix.
3. Repeat this procedure to locate all of the helical regions.
4. Identify regions where 3 out of 5 residues have P(b)>100: b-sheet. Extend
sheet in both directions until four contiguous residues have an average
P(b)<100: end of b-sheet. If P(b)>105 and P(b)>P(a): a-helix.
5. Rest: P(a)>P(b) a-helix. P(b)>P(a) b-sheet.
6. To identify a bend at residue number i, calculate the following value:
p(t) = f(i)f(i+1)f(i+2)f(i+3)
If: (1) p(t) > 0.000075; (2) average P(t)>1.00 in the tetrapeptide; and (3)
averages for tetrapeptide obey P(a)<P(t)>P(b): b-turn.
Secondary structure prediction:CHOU-FASMAN
61. Successful method?
19 proteins evaluated:
• Successful in locating 88% of helical and 95% of
b regions
• Correctly predicting 80% of helical and 86% of b-
sheet residues
• Accuracy of predicting the three conformational
states for all residues, helix, b, and coil, is 77%
Chou & Fasman:successful method
After 1974:improvement of preference parameters
Secondary structure prediction:CHOU-FASMAN
62.
63. Sander-Schneider: Evolution of overall structure
• Naturally occurring sequences with more than
20% sequence identity over 80 or more
residues always adopt the same basic
structure (Sander and Schneider 1991)
65. • SCOP:
– Structural Classification of
Proteins
• FSSP:
– Family of Structurally Similar
Proteins
• CATH:
– Class, Architecture, Topology,
Homology
Structural Family Databases
66. Levels of protein structure
• Tertiary
– Packing of secondary structure
elements into a compact spatial unit
– Fold or domain – this is the level to
which structure is currently possible
69. • Protein Dissection into domain
• Conserved Domain Architecture
Retrieval Tool (CDART) uses
information in Pfam and SMART to
assign domains along a sequence
• (automatic when blasting)
Domains
70. • From the analysis of alignment of protein
families
• Conserved sequence features, usually
associate with a specific function
• PROSITE database for protein
“signature” protein (large amount of FP &
FN)
• From aligment of homologous sequences
(PRINTS/PRODOM)
• From Hidden Markov Models (PFAM)
• Meta approach: INTERPRO
Domains
75. The ‘positive inside’ rule
(EMBO J. 5:3021; EJB 174:671,205:1207; FEBS lett. 282:41)
Bacterial IM
In: 16% KR out: 4% KR
Eukaryotic PM
In: 17% KR out: 7% KR
Thylakoid membrane
In: 13% KR out: 5% KR
Mitochondrial IM
In: 10% KR out: 3% KR
76.
77. • Membrane-bound receptors
• A very large number of different domains both to
bind their ligand and to activate G proteins.
• 6 different families
• Transducing messages as photons, organic odorants,
nucleotides, nucleosides, peptides, lipids and proteins.
GPCR Topology
• Pharmaceutically the most important class
• Challenge: Methods to find novel GCPRs in human genome
…
81. Levels of protein structure
• Difficult to predict
• Functional units: Apoptosome,
proteasome
82. Protein Structure
Introduction
Why ?
How do proteins fold ?
Levels of protein structure
0,1,2,3,4
X-ray / NMR
The Protein Database (PDB)
Protein Modeling
Bioinformatics & Proteomics
Weblems
83. • X-ray crystallography is an experimental
technique that exploits the fact that X-rays are
diffracted by crystals.
• X-rays have the proper wavelength (in the
Ångström range, ~10-8 cm) to be scattered by
the electron cloud of an atom of comparable
size.
• Based on the diffraction pattern obtained from
X-ray scattering off the periodic assembly of
molecules or atoms in the crystal, the electron
density can be reconstructed.
• A model is then progressively built into the
experimental electron density, refined against
the data and the result is a quite accurate
molecular structure.
What is X-ray Crystallography
84. • NMR uses protein in solution
– Can look at the dynamic properties of the protein structure
– Can look at the interactions between the protein and ligands,
substrates or other proteins
– Can look at protein folding
– Sample is not damaged in any way
– The maximum size of a protein for NMR structure determination is ~30
kDa.This elliminates ~50% of all proteins
– High solubility is a requirement
• X-ray crystallography uses protein crystals
– No size limit: As long as you can crystallise it
– Solubility requirement is less stringent
– Simple definition of resolution
– Direct calculation from data to electron density and back again
– Crystallisation is the process bottleneck, Binary (all or nothing)
– Phase problem Relies on heavy atom soaks or SeMet incorporation
• Both techniques require large amounts of pure protein and require
expensive equipment!
NMR or Crystallography ?
85. Protein Structure
Introduction
Why ?
How do proteins fold ?
Levels of protein structure
0,1,2,3,4
X-ray / NMR
The Protein Database (PDB)
Protein Modeling
Bioinformatics & Proteomics
Weblems
93. • Demonstration of Protein explorer
• PDB, install Chime
• Search helicase (select structure where
DNA is present)
• Stop spinning, hide water molecules
• Show basic residues, interact with
negatively charged backbone
• RASMOL / Cn3D
Visualizing Structures
94. Protein Structure
Introduction
Why ?
How do proteins fold ?
Levels of protein structure
0,1,2,3,4
X-ray / NMR
The Protein Database (PDB)
Protein Modeling
Bioinformatics & Proteomics
Weblems
97. • Finding a structural homologue
• Blast
–versus PDB database or PSI-
blast (E<0.005)
–Domain coverage at least 60%
• Avoid Gaps
–Choose for few gaps and
reasonable similarity scores
instead of lots of gaps and high
similarity scores
Modeling
98. • Extract “template” sequences and align with query
• Whatch out for missing data (PDB file) and complement with additonal
templates
• Try to get as much information as possible, X/NMR
• Sequence alignment from structure comparson of templates (SSA) can be
different from a simple sequence aligment
• >40% identity, any aligment method is OK
• <40%, checks are essential
– Residue conservation checks in functional regions (patterns/motifs)
– Indels: combine gaps separted by few resides
– Manual editing: Move gaps from secondary elements to loops
– Within loops, move gaps to loop ends, i.e. turnaround point of backbone
• Align templates structurally, extract the corresponding SSA or QTA
(Query/template alignment)
Modeling
99. Input for model building
• Query sequence (the one you want the 3D
model for)
• Template sequences and structures
• Query/Template(s) (structure) sequence
aligment
Modeling
100. • Methods (details on these see paper):
– WHATIF,
– SWISS-MODEL,
– MODELLER,
– ICM,
– 3D-JIGSAW,
– CPH-models,
– SDC1
Modeling
101. • Model evaluation (How good is the prediction,
how much can the algorithm rely/extract on
the provided templates)
– PROCHECK
– WHATIF
– ERRAT
• CASP (Critical Assessment of Structure
Prediction)
– Beste method is manual alignment editing !
Modeling
102. CASP4: overall model accuracy ranging from 1 Å to 6 Å for 50-10% sequence identity
**T112/dhso – 4.9 Å (348 residues; 24%) **T92/yeco – 5.6 Å (104 residues; 12%)
**T128/sodm – 1.0 Å (198 residues; 50%)
**T125/sp18 – 4.4 Å (137 residues; 24%)
**T111/eno – 1.7 Å (430 residues; 51%) **T122/trpa – 2.9 Å (241 residues; 33%)
Comparative modelling at CASP
CASP2
fair
~ 75%
~ 1.0 Å
~ 3.0 Å
CASP3
fair
~75%
~ 1.0 Å
~ 2.5 Å
CASP4
fair
~75%
~ 1.0 Å
~ 2.0 Å
CASP1
poor
~ 50%
~ 3.0 Å
> 5.0 Å
BC
excellent
~ 80%
1.0 Å
2.0 Å
alignment
side chain
short loops
longer loops